RAP: Phased Retrieval-Augmented Reasoning for Incident-Aware Traffic Prediction

Traffic prediction plays a pivotal role in route planning and traffic management. Most existing methods focus primarily on historical spatio-temporal dependencies, overlooking external disruptions, particularly non-recurrent traffic incidents that can substantially alter traffic patterns. However, the unpredictability and heterogeneous impacts of incidents make it difficult to extract transferable incident-related knowledge from historical data, limiting the ability to accurately capture incident-affected traffic dynamics. To address these challenges, we propose RAP, a phased retrieval-augmented reasoning framework for incident-aware traffic prediction. RAP explicitly models incident-induced dynamics by integrating retrieval-augmented mechanisms with Large Language Model (LLM)-based reasoning. Specifically, it comprises two core components: Historical Knowledge Discovery and Context-Aware Phased Reasoning. The former extracts three complementary forms of transferable evidence from historical traffic data: incident patterns, normal counterfactual sequences, and experience trajectories. The latter uses an LLM to assess and consolidate heterogeneous evidence into a compact context, then partitions the prediction horizon into distinct impact phases for phase-consistent prediction. Extensive experiments on three real-world datasets demonstrate that RAP consistently outperforms state-of-the-art baselines, showing up to 5.27% performance improvement in incident-affected scenarios. The code is available at: https://anonymous.4open.science/r/RAP-608C.